Clinicians and researchers continue to need better methods to gather biologically incisive data on retinal disease and in vivo pathology. There currently exists no satisfactory method or system to localize focal in vivo pathology that correlates with function. Retinal imaging with optical coherence tomography (OCT) has improved over the past decade and yields cross-sectional images of retinal morphology. (See Pieroni C G, Witkin A J, Ko T H, et al., “Ultrahigh resolution optical coherence tomography in non-exclusive age related macular degeneration,” Br J Ophthalmol 2006; 90(2): 191-7; and Massin Girach A, Erginay A, Gaudric A., “Optical coherence tomography: a key to the future management of patients with diabetic macular oedema,” Acta Ophthalmol Scand 2006; 84(4): 466-74.) Although clinicians are able to define pathologies on retinal OCT cross-sections based on previous clinicopathologic correlation, this cross-sectional information is viewed separately and not integrated with conventional fundus imaging such as color photography and angiography. Although thickness data calculated from cross-sectional scans have been converted and interpolated into surface maps of the macula or of nerve fiber layer thicknesses, these maps rely on location of scans as judged by fundus video images or on fixation. Consequently, they lack annotation of focal pathology.
Spectral domain optical coherence tomography (SD-OCT), also known as Fourier domain OCT, is a relatively new imaging technique that utilizes the Fourier transform function to gather depth data from the spectra of the OCT signal and thus eliminates the need to mechanically move the scanning mirror to obtain depth information as is required for commercially available time-domain systems. (See Huang D, Swanson E A, Lin C P, et al., “Optical coherence tomography,” Science 1991;254:1178-81; Puliafito C A, Hee M R, Lin C P, et al., “Imaging of macular diseases with optical coherence tomography,” Ophthalmology 1995;102:217-29; and Hee M R, Izatt J A, Swanson E A, et al., “Optical coherence tomography of the human retina,” Arch Ophthalmol 1995;113:325-32.) The SD-OCT technique significantly increases signal-to-noise ratio and increases the speed of data collection by a factor of 50 (conventional time-domain OCT functions at 400 A-scan/sec, while the SD-OCT system scans at 20,000 A-scan/sec). (See Wojtkowski M, Bajraszewski T, Gorczyńska I, et al., “Ophthalmic imaging by spectral optical coherence tomography,” Am J Ophthalmol 2004;138:412-9; Wojtkowski M, Leitgeb R, Kowalczyk A, et al., “In vivo human retinal imaging by Fourier domain optical coherence tomography,” J Biomed Opt 2002;7:457-63; and Wojtkowski M, Bajraszewski T, Targowski P, Kowalczyk A., “Real-time in vivo imaging by high-speed spectral optical coherence tomography,” Opt Lett 2003;28:1745-7.) Because of the increase in speed, a single cross-sectional scan of 1000 A-scans can be captured, processed, streamed to disk, and displayed in 60 ms (or 1/42 of the time required for a time-domain scan). Because of this speed, there is less eye movement during the SD-OCT scan and thus a more stable image with a significant decrease in artifact of the image caused by patient motion. Also because of this speed, a stack of 100 cross-sectional scans can be acquired in the time normally used to gather 6 low resolution cross-sectional scans of the macula on a time-domain system. The image stack across the macula can be processed to produce a three dimensional representation of structures. (See Wojtkowski M, Srinivasan V, Fujimoto J G, et al., “Three-dimensional retinal imaging with high-speed ultrahigh-resolution optical coherence tomography,” Ophthalmology 2005;112:1734-46.)
SD-OCT imaging thus frequently uses a series of scans. A resulting stack of B-scans can undergo further analysis and produce a three dimensional representation of structures. Furthermore, it is possible to collapse three dimensional OCT volumes (e.g., along a depth axis) to a two-dimensional representative image along any plane of a 3D volume using algorithms to calculate a single representative pixel intensity for each line in the projection. One technique of obtaining such an en face picture with optical coherence tomograms is referred to as a summed voxel projection (SVP). (See Jiao et al, “Simultaneous acquisition of sectional and fundus ophthalmic images with spectral-domain optical coherence tomography”, Optics Express 13:444-452 (2005)).
Even though some pathological structures can be observed on a two dimensional en face image produced with SVP, this technique may not show all the changes that might be relevant for the diagnosis because some information is lost. In particular, the SVP technique may not show relevant pathologies because much information is lost in the summing of the pixels in the collapsing process.
Accordingly, there is a need for an exact system/method to annotate, extract and preserve different pathological conditions and/or changes that are recognized on cross-sections within the three dimensional volume so that the findings are maintained (preserved as visible) in an en face projection produced with a SVP technique. Exemplary embodiments of the technology described herein resolve such a need.
Present exemplary embodiments provide a method/system to annotate, extract or preserve different pathological conditions and/or changes that are recognized on cross-sections within a three dimensional volume so that the findings are maintained (preserved as visible) in an en face projection produced with a SVP technique. Furthermore, present exemplary embodiment(s) make it possible to coregister marked changes with other types of two dimensional en face images such as images from other ophthalmic devices (e.g., angiography device, microperimetry device, autofluorescence device, fundal photography device, etc.). The findings are maintained in an image resulting from a coregistration of the projection produced with the SVP technique and the other types of two dimensional en face images.
In more detail, present exemplary embodiments delineate, extract and preserve different pathological conditions and/or changes that are recognized on retinal cross-sections obtained from patients with retinal disease. The patients may have, for example, neovascular and non neovascular age related macular degeneration (AMD). With present exemplary embodiments, the delineated pathology (e.g., pathology delineated, via color-coded markings or sets of numbers, by a user and/or automatically by an image processing, rendering and interpolation algorithm) remains visible through the SVP and coregistration process. Thus the lateral extent and location of pathology (as well as other features of the pathology such as thickness, volume, size and/or severity) is precisely maintained relative to retinal vasculature on fundus images produced with the SVP technique.
The present exemplary embodiments thus identify, quantify and locate pathologic conditions and/or changes in retinal cross-sections obtained with SD-OCT so that the findings are maintained when collapsed into a two-dimensional fundus image for comparison with other retinal studies. These findings are also maintained during coregistration of the SD-OCT image data with other retinal study (e.g., angiography, microperimetry, autofluorescence and/or fundal photography) data. Alignment of the SD-OCT and other study image data during their coregistration may be obtained via a common location (e.g., location of pathology or distinguishable vascular landmark) identified by the user and/or system.